Machine learning to classify clinically meaningful patient groups by ESAS symptom clusters in oropharyngeal cancer patients undergoing initial treatment.
Bibliographic record
Abstract
171 Background: Cancer patients often experience symptoms in clusters, which could contribute to patient outcomes and quality of life; yet most analyses describe only individual symptom scores or a summation of symptom scores rather than symptom clusters. We compared machine learning to traditional statistical methods for grouping patients by symptoms in a heavy symptom burden patient population: oropharyngeal cancer patients undergoing initial treatment with radiation or chemoradiation (C/RT). Methods: K-means clustering was compared to traditional statistical methods (i.e., histogram-analysis; use of quantiles; categorization by clinico-demographic features) for classifying patients based on Edmonton Symptom Assessment System (ESAS) symptom scores in newly diagnosed oropharyngeal cancer patients before and after C/RT initiation. Results: 278 patients were classified into 3 K-means groups at each of two timepoints: pre-C/RT and post-C/RT. These groupings were formed primarily based on differences by symptom burden and were thus labeled: low, moderate, and high symptom-burden patient groups. The table shows dynamic change as patients moved from group to group during C/RT treatment. Greatest symptom-burdens in pre-C/RT patients were anxiety and tiredness; in post-C/RT, poor appetite and tiredness. Chi-squared residuals attributed being female (residuals of -3.95 for low, -2.46 for moderate, and 2.41 for high burden) and HPV-negative cancer (residuals of -1.48, -0.52, and 2.57, respectively) to being associated with higher symptom burden pre-C/RT; no clinico-demographic characteristics were associated with high symptom burden post-C/RT, suggesting this was a previously unidentified patient group. Although separation into K-means clusters in this population was primarily related to symptom burden, K-means better identified the number of patient groups and classified patients at the boundaries between groups (involving up to a quarter of patients) when compared to traditional statistical methods. Conclusions: Machine learning clustering analysis separated patients into discrete groups by symptom burden. K-means provided an objective means for clustering patients in a clinically-meaningful way when compared to traditional statistical methods for grouping patients. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".